haxl 2.0.0.0 → 2.0.1.0

raw patch · 5 files changed

+41/−9 lines, 5 filesdep ~aesonPVP ok

version bump matches the API change (PVP)

Dependency ranges changed: aeson

API changes (from Hackage documentation)

Files

• Haxl/Core.hs +1/−1
• Haxl/Core/Memo.hs +26/−2
• changelog.md +7/−0
• haxl.cabal +3/−3
• readme.md +4/−3

@@ -31,7 +31,7 @@      -- ** Memoization   , newMemo, newMemoWith, prepareMemo, runMemo-  , memo, memoize, memoize1, memoize2+  , memo, memoUnique, memoize, memoize1, memoize2   , memoFingerprint, MemoFingerprintKey(..)      -- ** Conditionals

@@ -30,8 +30,10 @@   , memoFingerprint   , MemoFingerprintKey(..)   , memoize, memoize1, memoize2+  , memoUnique      -- * Local memoization+  , MemoVar   , newMemo   , newMemoWith   , prepareMemo@@ -302,12 +304,24 @@   => k -> GenHaxl u a -> GenHaxl u a memo key = cachedComputation (MemoKey key) -{-# RULES "memo/Text"-  memo = memoText :: (Typeable a) => Text -> GenHaxl u a -> GenHaxl u a+{-# RULES+"memo/Text" memo = memoText :: (Typeable a) =>+            Text -> GenHaxl u a -> GenHaxl u a+"memoUnique/Text" memoUnique = memoUniqueText :: (Typeable a) =>+                  MemoFingerprintKey a -> Text -> GenHaxl u a -> GenHaxl u a  #-}  {-# NOINLINE memo #-} +-- | Memoize a computation using its location and a Fingerprint. This ensures+-- uniqueness across computations.+memoUnique+  :: (Typeable a, Typeable k, Hashable k, Eq k)+  => MemoFingerprintKey a -> k -> GenHaxl u a -> GenHaxl u a+memoUnique fp key = memo (fp, key)++{-# NOINLINE memoUnique #-}+ data MemoKey k a where   MemoKey :: (Typeable k, Hashable k, Eq k) => k -> MemoKey k a   deriving Typeable@@ -331,6 +345,16 @@  memoText :: (Typeable a) => Text -> GenHaxl u a -> GenHaxl u a memoText key = withLabel key . cachedComputation (MemoText key)++-- For Text keys, memoUnique is automatically rewritten to memoUniqueText,+-- due to the RULES pragma above.+memoUniqueText+  :: (Typeable a)+  => MemoFingerprintKey a+  -> Text+  -> GenHaxl u a+  -> GenHaxl u a+memoUniqueText fp key = withLabel key . memo (fp, key)  -- | A memo key derived from a 128-bit MD5 hash.  Do not use this directly, -- it is for use by automatically-generated memoization.

@@ -1,3 +1,10 @@+# Changes in version 2.0.1.0++  * Exported MemoVar from Haxl.Core.Memo+  * Updated the facebook example+  * Fixed some links in the documentation+  * Bump some version bounds+ # Changes in version 2.0.0.0    * Completely rewritten internals to support arbitrarily overlapping

@@ -1,5 +1,5 @@ name:                haxl-version:             2.0.0.0+version:             2.0.1.0 synopsis:            A Haskell library for efficient, concurrent,                      and concise data access. homepage:            https://github.com/facebook/Haxl@@ -43,7 +43,7 @@ library    build-depends:-    aeson >= 0.6 && < 1.4,+    aeson >= 0.6 && < 1.5,     base == 4.*,     binary >= 0.7 && < 0.10,     bytestring >= 0.9 && < 0.11,@@ -102,7 +102,7 @@ test-suite test    build-depends:-    aeson >= 0.6 && < 1.3,+    aeson,     HUnit >= 1.2 && < 1.7,     base == 4.*,     binary,

@@ -7,7 +7,8 @@   * batch multiple requests to the same data source,  * request data from multiple data sources concurrently,- * cache previous requests.+ * cache previous requests,+ * memoize computations.  Having all this handled for you behind the scenes means that your data-fetching code can be much cleaner and clearer than it would@@ -37,7 +38,7 @@    explains how Haxl came about at Facebook, and discusses our    particular use case. - * [An example Facebook data source](example/facebook/readme.md) walks+ * [An example Facebook data source](https://github.com/facebook/Haxl/blob/master/example/facebook/readme.md) walks    through building an example data source that queries the Facebook    Graph API concurrently. @@ -45,7 +46,7 @@    Walks through using Haxl from scratch for a simple SQLite-backed    blog engine. - * [The N+1 Selects Problem](example/sql/readme.md) explains how Haxl+ * [The N+1 Selects Problem](https://github.com/facebook/Haxl/blob/master/example/sql/readme.md) explains how Haxl    can address a common performance problem with SQL queries by    automatically batching multiple queries into a single query,    without the programmer having to specify this behavior.